Authors: Naman Srivastava, Joel D Joy,Yash Dixit, Swarup E, Rakshit Ramesh Know more Land Use Land Cover (LULC) mapping is essential for urban and resource planning, and is one of the key elements in developing smart and sustainable this http URL study evaluates advanced LULC mapping techniques, focusing on Look-Up Table (LUT)-based Atmospheric Correction applied to Cartosat […]

Authors: Jaheer khan, Swarup E, Rakshit Ramesh Know more This evaluate the performance of geospatial image processing using two distinct data storage formats: Zarr and TIFF. Geospatial images, converted to numerous applications like environmental monitoring, urban planning, and disaster management. Traditional Tagged Image File Format is mostly used because it is simple and compatible but […]

Authors: Jaheer khan, Swarup E, Rakshit Ramesh Know more This evaluates the performance of geospatial image processing using two distinct data storage formats: Zarr and TIFF. Geospatial images, converted to numerous applications like environmental monitoring, urban planning, and disaster management. Traditional Tagged Image File Format is mostly used because it is simple and compatible but […]

Authors: Yash Dixit, Naman Srivastava, Joel D Joy, Rohan Olikara, Swarup E, Rakshit Ramesh Know more Land Use Land Cover (LULC) mapping is a vital tool for urban and resource planning, playing a key role in the development of innovative and sustainable cities. This study introduces a semi-supervised segmentation model for LULC prediction using high-resolution […]

Authors: Soham Mukherjeea, Yash Dixit, Naman Srivastava, Joel D Joy, Koesha Sinha, Rohan Olikara, Swarup E, Rakshit Ramesh Know more The integration of fine-scale multispectral imagery with deep learning models has revolutionized land use and land cover (LULC) classification. However, the atmospheric effects present in Top-of-Atmosphere sensor measured Digital Number values must be corrected to […]

Authors: Shivika Sharma, Nandini Mawane, Chetan Kumar Kuraganti, Dhruthick Gowda M, Mayur Taware, Yash Chandrashekhar Dixit, Sahil Mishra, Raghu Krishnapuram and Rakshit Ramesh Know more Accurate Estimated Time of Arrival (ETA) predictions play a critical role in improving the operational efficiency, traffic management, and reliability of services in transit systems. This work presents a comprehensive […]

Authors: Chetan Kumar Kuraganti, Arun Josephraj, Saanidhya Vats, Amarthya KR, Koesha Sinha, Raghu Krishnapuram and Rakshit Ramesh Know more This paper presents two techniques for optimizing solid waste management (SWM) systems of a city. The first technique involves planning routes for garbage collection vehicles which utilizes the Capacitated Vehicle Routing Problem (CVRP) and Google-OR-Tools to […]

Authors: Debarun Sengupta, Anjula Gurtoo, Minnu Malieckal, Jyotirmoy Dutta Download PDF Large amounts of data get generated and applied in decision making to improve outcomes. However, quality of the data remains an issue as data gets generated from varied sources, in unspecified formats, and variables vary across different types of data. Identifying key quality dimensions […]

Authors: Bita Afsharinia, Anjula Gurtoo, Jyotirmoy Dutta, Minnu Malieckal Download PDF The study aims to critically evaluate current privacy-preserving technologies and ethical frameworks in data sharing, identifying gaps and proposing a comprehensive, integrated ethical framework. Existing frameworks often fall short in integrating these two aspects effectively, particularly in the context of emerging technologies. The study […]

Authors: Bita Afsharinia, Anjula Gurtoo, Jyotirmoy Dutta, Minnu Malieckal Download PDF This report provides an overview of the key concepts and techniques in privacy-preserving data analysis, emphasizing the importance of differential privacy, data anonymization, and data perturbation. The privacy-preserving principles of autonomy, justice, non-maleficence, beneficence, and explicability are critical for maintaining ethical standards in data […]

Authors: Anjula Gurtoo, Jyotirmoy Dutta Download PDF The data governance panel during the 24 November 2023 Symposium on Data for Public Good at the Indian Institute of Science debated pertinent issues in the quest for a pragmatic policy framework for data governance. This document details and collates the views of different stakeholders.

Authors: Minnu Malieckal, Anjula Gurtoo, Jyotirmoy Dutta, Linda Theres B, Sandeep P Download PDF This document is intended as a playbook for the geospatial sector to better understand what is meant by data, its use and how to manage its quality. The playbook details domains and datasets in geospatial sector, types of data, data formats, […]

On the Optimal Number of Grids for Differentially Private Non-Interactive K-Means Clustering Authors: Gokularam M, Anshoo Tandon Differentially private K-means clustering enables releasing cluster centers derived from a dataset while protecting the privacy of the individuals. Non-interactive clustering techniques based on privatized histograms are attractive because the released data synopsis can be reused for other downstream […]

Authors:K. Reddy, N. Chakraborty, A. Dharmavaram, A. Tandon Know more Data privacy and anonymisation are critical concerns in today’s data-driven society, particularly when handling personal and sensitive user data. Regulatory frameworks worldwide recommend privacy-preserving protocols such as k-anonymisation to de-identify releases of tabular data. Available hardware resources provide an upper bound on the maximum size […]

Authors:V. A. Rameshwar and A. Tandon Know more This paper considers the private release of statistics of several disjoint subsets of a datasets. In particular, we consider the epsilon-user-level differentially private release of sample means and variances of sample values in disjoint subsets of a dataset, in a potentially sequential manner. Traditional analysis of the privacy […]

Authors:V. A. Rameshwar and A. Tandon Know more In this paper, we consider the problem of degradation of anonymity upon linkages of anonymized datasets. We work in the setting where an adversary links together tgeq 2 anonymized datasets in which a user of interest participates, based on the user’s known quasi-identifiers, which motivates the use of ell-diversity as […]

Authors: V. Arvind Rameshwar (IIT Madras) and Anshoo Tandon Know more We revisit the problem of releasing the sample mean of bounded samples in a dataset, privately, under user-level ε-differential privacy (DP). We aim to derive the optimal method of preprocessing data samples, within a canonical class of processing strategies, in terms of the estimation error. […]

Authors: Gokularam M, Anshoo Tandon Know more Differentially private K-means clustering enables releasing cluster centers derived from a dataset while protecting the privacy of the individuals. Non-interactive clustering techniques based on privatized histograms are attractive because the released data synopsis can be reused for other downstream tasks without additional privacy loss. The choice of the […]

Authors: Bryan Paul Robert, Mahidhar Chellamani, Jyotirmoy Dutta Know more For years, some of India’s most valuable geospatial datasets remained scattered across government departments, research institutes, or private organizations. They held immense potential to transform logistics, strengthen climate resilience, and support smarter urban planning, but they remained difficult to access, buried in different formats and […]

Authors: M. Yashwanth, G. K. Nayak, A. Singh, Y. Simmhan, A. Chakraborty Know more Federated Learning (FL) is a machine learning paradigm that enables clients to jointly train a global model by aggregating the locally trained models without sharing any local training data. In practice, there can often be substantial heterogeneity (e.g., class imbalance) across […]

Authors: N. Chakraborty, A. Sharma, J. Dutta. H. D. Kumar Know more This paper proposes a novel framework for automated, objective, and privacy-preserving data quality assessment of time-series data from IoT sensors deployed in smart cities. We leverage custom, autonomously computable metrics that parameterise the temporal performance and adherence to a declarative schema document to […]

Authors: N. Chakraborty, A. Tandon, K. Reddy, K. Kirpekar, B. Robert, H. Kumar, A. Venkatesh, A. Sharma Know more Data de-identification makes it possible to glean insights from data while preserving user privacy. The use of Trusted Execution Environments (TEEs) allow for the execution of de-identification applications on the cloud without the need for a […]

Authors: V. A. Rameshwar, A. Tandon, and A. Sharma Know more This paper considers the epsilon-differentially private (DP) release of an approximate cumulative distribution function (CDF) of the samples in a dataset. We assume that the true (approximate) CDF is obtained after lumping the data samples into a fixed number K of bins. In this […]

Authors: H. Kallamadi, R. Burra, S. Mittal, S. Sharma, A. Venkatesh, A. Tandon Know more This work contributes towards the development of an efficient and scalable open-source Secure Multi-Party Computation (SMPC) protocol on machines with moderate computational resources. We use the ABY2.0 SMPC protocol implemented on the C++ based MOTION2NX framework for secure convolutional neural […]